Generally, causal inference applied to complex biological systems relay
on models that are either a priori conceptual constructs given by the expert
or curated knowledge-based networks from biological repositories (typically
molecular, genetic, or protein-protein interaction databases)
[1]}, [2]}.
On the other hand, fully data-driven networks provide exploratory structures
unravelling hidden knowledge, although they can be deeply affected by
technical variability, and the specific method used to build them often
results in very different or irreproducible networks [1]}.
SEMgraph offers three methods to cope with these limitations, improving
the initial model by leveraging on both knowledge-based and data-driven
procedures. Firstly, SEMdag() uses data and topological information
from the input network to estimate the optimal directed (i.e., causal)
edge backbone. In addition, SEMbap() uses missing edges from the
input graph to search for bidirected edges (i.e., covariances) based on
conditional independence tests, removing possible latent sources of
confounding, encoded in the estimated covariance matrix. Finally,
extendGraph() uses external interactomes (e.g., from a chosen
biological database) and observed data to extend the input graph with
new connectors. The next sections will dive into the details of these
core functions.
